This invention relates to improvements in an apparatus for estimating a demand such as a traffic volume or an electric power load.
The traffic volume of elevators in a building, the electric power load of a power station, or the like (hereinbelow, simply termed "demand") fluctuates irregularly when closely observed within a period of one day, but presents similar aspects for the same time zones when observed over several days. In, for example, an office building, elevator passengers on their way to their office floors crowd on the first floor during a short period of time in the time zone in which they attend offices in the morning. In the first half of the lunch hour, many passengers go from the office floors to a restaurant floor, while in the latter half thereof, many passengers go from the restaurant floor to the office floors. In addition, many passengers go from the office floors to the first floor in the time zone in which they leave the offices in the evening. The volumes of traffic in the up direction and in the down direction are nearly equal in the daytime time zones other than mentioned above, while the volume of traffic becomes very small throughout the nighttime.
In order to deal with the traffic in the building changing in this manner by means of a limited number of elevators, the elevators are usually operated under group supervision. When a hall call is registered anew, it is tentatively assigned to respective elevators, and the waiting times of all hall calls, the possibility of the full capacity of passengers, etc., are predicted so as to select from among the elevators the optimum one to respond to the new hall call. In order to execute such predictive calculations, traffic data peculiar to each building is required. For example, data on the number of passengers who get on and off the cage of each elevator at intermediate floors is required for predicting the possibility of full capacity. When such traffic data which changes every moment is stored each time, an enormous memory capacity is necessitated, which is not practical. Usually, the required memory size is reduced by dividing the operating period of time in one day into several time zones and storing only the average traffic volumes of the respective time zones. After the completion of the building, however, there is a possibility that traffic data will change in accordance with changes in personnel organization in the building, and hence, it is difficult to obtain good traffic data with which the demand can be predicted accurately. For this reason, a system has been developed, for example as disclosed in copending application Ser. No. 473,359 filed Mar. 8, 1983 now U.S. Pat. No. 4,567,566 and U.S. Pat. No. 4,524,418 wherein traffic conditions in the building are detected so as to sequentially improve the traffic data.
More specifically, the operating period of time in one day is divided into K time zones (hereinbelow, termed "sections"), and a time (hereinbelow, termed "boundary") by which a section k-1 and a section k are bounded is denoted by t.sub.k (k=2, 3, . . ., K). Times t.sub.l and t.sub.k+l are the starting time and end time of the elevator operation, respectively. The average traffic volume P.sub.k (l) of the section k on the l-th day is given by the following Equation (1): ##EQU1##
Here, X.sub.k.sup.u (l) is a column vector of F-1 dimensions (where F denotes the number of floors) the elements of which are the number of passengers to get on cages in the up direction at respective floors in the time zone k of the l-th day. Similarly, X.sub.k.sup.d (l), Y.sub.k.sup.u (l) and Yhd k.sup.k (l) are column vectors which indicate the number of passengers to get on the cages in the down direction, the number of passengers to get off the cages in the up direction and the number of passengers to get off the cages in the down direction, respectively. The average traffic volume (hereinbelow, termed "average demend") P.sub.k (l) is measured by a passenger-number detector which utilizes load changes during the stoppage of the cages of the elevators and/or industrial television, ultrasonic wave, or the like.
First, the case where the representative value of the average demand P.sub.k (l) of each time zone is sequencially corrected in a case where the boundary t.sub.k is fixed is considered.
It is thought that the columns {P.sub.k (1), P.sub.k (2), . . .}of the average demands occurring daily will disperse in the vicinity of a certain representative value P.sub.k. Since the magnitude of the representative value P.sub.k is unknown, it needs to be estimated by any method. In this case, there is the possibility that the magnitude of the representative value P.sub.k will change. The representative value is therefore predicted by taking a linear weighted average given in Equations (2) and (3) below, whereby more importance is attached to the average demand P.sub.k (l) measured latest, than to the other average demands P.sub.k( 1), P.sub.k (2), . . . and P.sub.k (l-1). ##EQU2##
Here, P.sub.k (l) is the representative value which has been predicted from the average demands P.sub.k (l), ..., and P.sub.k (l) measured till the l-th day, and P.sub.k (O) is an initial value which is set at a suitable value in advance. .lambda..sub.i denotes the weight of the average demand P.sub.k (i) measured on the i-th day, and this weight changes depending upon a parameter a. More specifically, an increase in the value of the parameter a results in an estimation in which more importance is attached to the latest measured average demand P.sub.k (l) than to the other average demands P.sub.k (1), ... and Pk(l-1), and in which the predictive representative value P.sub.k (l) quickly follows up the change of the representative value P.sub.k . However, when the value of the parameter a is too large, it is feared that the predictive representative value will change too violently in a manner to be influenced by the random variation of daily data. Meanwhile, Equations (2) and (3) can be rewritten as follows: EQU P.sub.k (l)=(1-a)P.sub.k (l-1)+a P.sub.k (l) (4) EQU P.sub.k (O)=P.sub.k (O) (5)
In accordance with the above Equation (4), there is the advantage that the weighted average of Equation (2) can be calculated without storing the observation values P.sub.k (i)(i=1, 2, ..., l-1) of the average demands in the past.
However, even in case of a demand which fluctuates cyclically, when the demand is observed over a long term, the representative value P.sub.k thereof might change greatly without remaining constant. By way of example, the traffic volume of elevators in a building is small at first after the completion of the building because there are comparatively few residents. The traffic volume increases little by little with the lapse of time, but some period is taken before the traffic volume becomes stable. In addition, in case of a building for rent, even when a considerable period of time has lapsed after the completion of the building, the residents sometimes change suddenly. Also in this case, the representative value P.sub.k of the demand changes.
In a case where, even when the magnitude itself of the representative value P.sub.k of the demand has changed greatly as described above, the predictive representative value P.sub.k (l) of the representative value P.sub.k of the demand is calculated by the use of the parameter a which is set at a small value so as to avoid the influence of random variations in daily data, and therefore cannot follow changes in the representative value P.sub.k quickly and, therefore, greatly deviates from the actual demand. In consequence, the calculations of the waiting time and the possibility of full capacity being wrongly predicted arises, and the elevators are not group-supervised as intended. Conversely, when the parameter a is set at a large value so as to permit the predictive representative value P.sub.k (l) to quickly follow the representative value P.sub.k, the predictive representative value P.sub.k (l) changes violently due to the influence of random variation in daily data during the stable period of the representative value P.sub.k, so that similar inconveniences arise.